U.S. patent application number 15/307393 was filed with the patent office on 2017-02-23 for method and system for determining a position relative to a digital map.
The applicant listed for this patent is TomTom Global Content B.V.. Invention is credited to Rafal Jan Gliszczynski, Blazej Kubiak, Krzysztof Miksa.
Application Number | 20170052032 15/307393 |
Document ID | / |
Family ID | 50972142 |
Filed Date | 2017-02-23 |
United States Patent
Application |
20170052032 |
Kind Code |
A1 |
Miksa; Krzysztof ; et
al. |
February 23, 2017 |
METHOD AND SYSTEM FOR DETERMINING A POSITION RELATIVE TO A DIGITAL
MAP
Abstract
A method of determining a longitudinal position of a vehicle
(100) relative to a digital map is disclosed in which real time
scan data (200, 202) is determined by scanning a lateral
environment around the vehicle using at least one range-finder
sensor, said real time scan data comprising one or more depth maps,
each depth map representing the measured lateral distance to
surfaces in the lateral environment for a plurality of longitudinal
positions and elevations. Localisation reference data associated
with the digital map for a deemed current longitudinal position of
the vehicle (100) in relation to the digital map is retrieved, and
compared to the real time scan data (200, 202) by calculating a
cross-correlation to determine a longitudinal offset. The deemed
current longitudinal position is adjusted based on said
longitudinal offset to determine the longitudinal position of the
vehicle (100) relative to the digital map.
Inventors: |
Miksa; Krzysztof; (Lodz,
PL) ; Gliszczynski; Rafal Jan; (Lodz, PL) ;
Kubiak; Blazej; (Lodz, PL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
TomTom Global Content B.V. |
Amsterdam |
|
NL |
|
|
Family ID: |
50972142 |
Appl. No.: |
15/307393 |
Filed: |
April 30, 2015 |
PCT Filed: |
April 30, 2015 |
PCT NO: |
PCT/EP2015/059614 |
371 Date: |
October 28, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01C 21/30 20130101;
G06T 7/50 20170101; G01C 21/28 20130101; G06T 2207/30252 20130101;
G06T 2207/10004 20130101 |
International
Class: |
G01C 21/30 20060101
G01C021/30; G06T 7/00 20060101 G06T007/00 |
Foreign Application Data
Date |
Code |
Application Number |
Apr 30, 2014 |
GB |
1407643.4 |
Claims
1. A method of determining a longitudinal position of a vehicle
relative to a digital map, the digital map comprising data
representative of navigable elements of a navigable network along
which the vehicle is travelling, the method comprising: determining
real time scan data by scanning a lateral environment around the
vehicle using at least one range-finder sensor, said real time scan
data comprising one or more depth maps, each depth map representing
the measured lateral distance to surfaces in the lateral
environment for a plurality of longitudinal positions and
elevations; retrieving localisation reference data associated with
the digital map for a deemed current longitudinal position of the
vehicle in relation to the digital map, wherein the localisation
reference data comprises one or more depth maps, each depth map
representing the lateral distance to surfaces in the lateral
environment for a plurality of longitudinal positions and
elevations; comparing the real time scan data to the localisation
reference data by calculating a cross-correlation to determine a
longitudinal offset between the real time scan data and the
localisation reference data; and adjusting the deemed current
longitudinal position based on said longitudinal offset to
determine the longitudinal position of the vehicle relative to the
digital map.
2. The method as claimed in claim 1, wherein the real time scan
data is obtained by scanning the lateral environment on both on a
left side of the vehicle and a right side of the vehicle so as to
determine a depth map for the left side of the vehicle and a depth
map for the right side of the vehicle that are combined into a
composite image.
3. The method as claimed in claim 1, wherein the localisation
reference data comprises a depth map representing the lateral
distance to surfaces in the lateral environment on the left of the
vehicle for a plurality of longitudinal positions and elevations
and a depth map representing the lateral distance to surfaces in
the lateral environment on the right of the vehicle for a plurality
of longitudinal positions and elevations.
4. The method as claimed in claim 3, wherein the depth map
representing the lateral distance to surfaces in the lateral
environment on the left of the vehicle for a plurality of
longitudinal positions and elevations and the depth map
representing the lateral distance to surfaces in the lateral
environment on the right of the vehicle for a plurality of
longitudinal positions and elevations are combined into a composite
image.
5. The method as claimed in claim 4, wherein the real time scan
data is obtained by scanning the lateral environment on both on a
left side of the vehicle and a right side of the vehicle so as to
determine a depth map for the left side of the vehicle and a depth
map for the right side of the vehicle that are combined into a
composite image, and the cross-correlation is calculated between
the composite image of the real time scan data and the image of the
localisation reference data.
6. The method as claimed in claim 1, wherein the one or more depth
maps of the localisation reference data are stored as compressed
images.
7. The method as claimed in claim 1, wherein the one or more depth
maps of the localisation reference data are stored as greyscale
images.
8. The method as claimed in claim 1, wherein the comparison of the
real time scan data to the localisation reference data is performed
using real time scan data obtained during a window of a
predetermined longitudinal distance.
9. The method as claimed in claim 8, wherein the comparison is
performed periodically for overlapping windows.
10. The method as claimed in claim 1, wherein the range-finder
sensor comprises one or more of: a laser scanner; a radar scanner;
and a pair of stereo cameras.
11. The method as claimed in claim 1, wherein the localisation
reference data is stored locally in a memory of the vehicle.
12. A device for determining a longitudinal position of a vehicle
relative to a digital map, the digital map comprising data
representative of navigable elements of a navigable network along
which the vehicle is travelling, the device comprising at least one
processor and a memory with computer readable instructions
executable by the at least one processor to cause the device to:
determine real time scan data by scanning a lateral environment
around the vehicle using at least one range-finder sensor, said
real time scan data comprising one or more depth maps, each depth
map representing the measured lateral distance to surfaces in the
lateral environment for a plurality of longitudinal positions and
elevations; retrieve localisation reference data associated with
the digital map for a deemed current longitudinal position of the
vehicle in relation to the digital map, wherein the localisation
reference data comprises one or more depth maps, each depth map
representing the lateral distance to surfaces in the lateral
environment for a plurality of longitudinal positions and
elevations; compare the real time scan data to the localisation
reference data by calculating a cross-correlation to determine a
longitudinal offset between the real time scan data and the
localisation reference data; and adjust the deemed current
longitudinal position based on said longitudinal offset to
determine the longitudinal position of the vehicle relative to the
digital map.
13. (canceled)
14. A non-transitory computer readable medium comprising computer
readable instructions which, when executed by at least one
processor of a device, cause the device to perform the method
according to claim 12.
Description
FIELD OF THE INVENTION
[0001] This invention relates to methods and systems for improved
positioning accuracy relative to a digital map, and which is needed
for highly and fully automated driving applications. More
specifically, embodiments of the invention, relate to the
generation of reference data (e.g. through crowd sourcing
techniques), the format of the reference data, and the use of the
reference data through a comparison to sensed data from a vehicle
to accurately position the vehicle on the digital map.
BACKGROUND OF THE INVENTION
[0002] It has become common in recent years for vehicles to be
equipped with navigation devices, either in the form of portable
navigation devices (PNDs) that can be removably positioned within
the vehicle or systems that are integrated into the vehicle. These
navigation devices comprise a means for determining the current
position of the device; typically a global navigation satellite
system (GNSS) receiver, such as GPS or GLONASS. It will be
appreciated, however, that other means may be used, such as using
the mobile telecommunications network, surface beacons or the
like.
[0003] Navigation devices also have access to a digital map
representative of a navigable network on which the vehicle is
travelling. The digital map (or mathematical graph, as it is
sometimes known), in its simplest form, is effectively a database
containing data representative of nodes, most commonly
representative of road intersections, and lines between those nodes
representing the roads between those intersections. In more
detailed digital maps, lines may be divided into segments defined
by a start node and end node. These nodes may be "real" in that
they represent a road intersection at which a minimum of 3 lines or
segments intersect, or they may be "artificial" in that they are
provided as anchors for segments not being defined at one or both
ends by a real node to provide, among other things, shape
information for a particular stretch of road or a means of
identifying the position along a road at which some characteristic
of that road changes, e.g. a speed limit. In practically all modern
digital maps, nodes and segments are further defined by various
attributes which are again represented by data in the database. For
example, each node will typically have geographical coordinates to
define its real-world position, e.g. latitude and longitude. Nodes
will also typically have manoeuvre data associated therewith, which
indicate whether it is possible, at an intersection, to move from
one road to another; while the segments will also have associated
attributes such as the maximum speed permitted, the lane size,
number of lanes, whether there is a divider in-between, etc. For
the purposes of this application, a digital map of this form will
be referred to as a "standard map".
[0004] Navigation devices are arranged to be able to use the
current position of the device, together with the standard map, to
perform a number of tasks, such as guidance with respect to a
determined route, and the provision of traffic and travel
information relative to the current position or predicted future
position based on a determined route.
[0005] It has been recognised, however, that the data contained
within standard maps is insufficient for various next generation
applications, such as highly automated driving in which the vehicle
is able to automatically control, for example, acceleration,
braking and steering without input from the driver, and even fully
automated "self-driving" vehicles. For such applications, a more
precise digital map is needed. This more detailed digital map
typically comprises a three-dimensional vector model in which each
lane of a road is represented separately, together with
connectivity data to other lanes. For the purposes of this
application, a digital map of this form will be referred to as a
"planning map" or "high definition (HD) map".
[0006] An representation of a portion of a planning map is shown in
FIG. 1, wherein each line represents the centreline of a lane. FIG.
2 shows another exemplary portion of a planning map, but this time
overlaid on an image of the road network. The data within these
maps is typically accurate to within a metre, or even less, and can
be collected using various techniques.
[0007] One exemplary technique for collecting the data to build
such planning maps is to use mobile mapping systems; an example of
which is depicted in FIG. 3. The mobile mapping system 2 comprises
a survey vehicle 4, a digital camera 40 and a laser scanner 6
mounted on the roof 8 of the vehicle 4. The survey vehicle 2
further comprises a processor 10, a memory 12 and a transceiver 14.
In addition, the survey vehicle 2 comprises an absolute positioning
device 2, such as a GNSS receiver, and a relative positioning
device 22 including an inertial measurement unit (IMU) and a
distance measurement instrument (DMI). The absolute positioning
device 20 provides geographical coordinates of the vehicle, and the
relative positioning device 22 serves to enhance the accuracy of
the coordinates measured by the absolute positioning device 20 (and
to replace the absolute positioning device in those instances when
signals from the navigation satellites cannot be received). The
laser scanner 6, the camera 40, the memory 12, the transceiver 14,
the absolute positioning device 20 and the relative positioning
device 22 are all configured for communication with the processor
10 (as indicated by lines 24). The laser scanner 6 is configured to
scan a laser beam in 3D across the environment and to create a
point cloud representative of the environment; each point
indicating the position of a surface of an object from which the
laser beam is reflected. The laser scanner 6 is also configured as
a time-of-flight laser range-finder so as to measure a distance to
each position of incidence of the laser beam on the object
surface.
[0008] In use, as shown in FIG. 4, the survey vehicle 4 travels
along a road 30 comprising a surface 32 having road markings 34
painted thereon. The processor 10 determines the position and the
orientation of the vehicle 4 at any instant of time from position
and orientation data measured using the absolute positioning device
20 and the relative positioning device 22, and stores the data in
the memory 12 with suitable timestamps. In addition, the camera 40
repeatedly captures images of the road surface 32 to provide a
plurality of road surface images; the processor 10 adding a
timestamp to each image and storing the images in the memory 12.
The laser scanner 6 also repeatedly scans the surface 32 to provide
at least a plurality of measured distance values; the processor
adding a timestamp to each distance value and stores them in the
memory 12. Examples of the data obtained from the laser scanner 6
are shown in FIGS. 5 and 6. FIG. 5 shows a 3D view, and FIG. 6
shows a side view projection; the colour in each picture being
representative of the distance to the road. All the data obtained
from these mobile mapping vehicles can be analysed and used to
create planning maps of the portions of the navigable (or road)
network travelled by the vehicles.
[0009] It has been recognised by the Applicant that in order to use
such planning maps for highly and fully automated driving
applications, it is necessary to know the position of a vehicle
relative to the planning map to a high degree of accuracy. The
traditional technique of determining the current location of a
device using navigation satellites or terrestrial beacons provides
an absolute position of the device to an accuracy of around 5-10
metres; this absolute position is then matched to a corresponding
position on the digital map. While this level of accuracy is
sufficient for most traditional applications, it is not
sufficiently accurate for next generations applications, where
positions relative to the digital map are required at sub-metre
accuracy even when travelling at high speeds on the road network.
An improved positioning method is therefore required.
SUMMARY OF THE INVENTION
[0010] In accordance with a first aspect of the present invention
there is provided a method of continually determining a
longitudinal position of a vehicle relative to a digital map; the
digital map comprising data representative of navigable elements
(e.g. roads) of a navigable network (e.g. road network) along which
the vehicle is travelling. The method comprises receiving real time
scan data obtained by scanning a lateral environment around the
vehicle; retrieving localisation reference scan data associated
with the digital map for a deemed current longitudinal position of
the vehicle in relation to the digital map, wherein the
localisation reference scan data comprises a reference scan of the
lateral environment around the deemed current longitudinal
position, optionally wherein said reference scan has been obtained
throughout the digital map from at least one device which has
previously travelled along the route; comparing the real time scan
data to the localisation reference scan data to determine a
longitudinal offset between the real time scan data and the
localisation reference scan data; and adjusting the deemed current
longitudinal position based on said longitudinal offset.
[0011] Thus, in accordance with the first aspect of the present
invention, the position of the vehicle relative to the digital map
can therefore always be known to a high degree of accuracy.
Examples in the prior art have attempted to determine the position
of a vehicle by comparing collected data with known reference data
for pre-determined landmarks along a route. However, the landmarks
may be sparsely distributed on many routes, resulting in
significant errors in the estimation of the vehicle's position when
it is travelling between the landmarks. This is a problem in
situations such as highly automated driving systems, where such
errors can cause catastrophic consequences such as vehicle crash
incidents resulting in serious injury or loss of life. The first
aspect of the present invention solves this problem by having
reference scan data throughout the digital map and by scanning the
lateral environment around the vehicle in real time. In this way,
the first aspect of the present invention allows real time scan and
reference data to be compared such that the position of the vehicle
relative to the digital map is always known to a high degree of
accuracy.
[0012] The deemed current longitudinal position can be obtained, at
least initially, from an absolute positioning system, such as a
satellite navigation device, such as GPS, GLONASS, the European
Galileo positioning system, COMPASS positioning system or IRNSS
(Indian Regional Navigational Satellite System). It will be
appreciated, however, that other location determining means can be
used, such as using the mobile telecommunications, surface beacons
or the like.
[0013] The digital map may comprise a three dimensional vector
model representing the navigable elements of the navigable network,
e.g. roads of the road network, in which each lane of the navigable
elements, e.g. roads, are represented separately. Thus, a lateral
position of the vehicle on the road may be known by determining the
lane in which the vehicle is travelling. In other embodiments, the
lateral position of the vehicle can be determined using a
comparison of the real time scan data with the retrieved
localisation reference data, as is discussed in more detail
below.
[0014] The real time scan data may be obtained on a left side of
the vehicle and a right side of the vehicle. This helps to reduce
the effect of transient features on the position estimation. Such
transient features may be, for example, parked vehicles, overtaking
vehicles or vehicles travelling the same route in the opposite
direction. Thus, real time scan data can record features present on
both sides of the vehicle. In some embodiments, the real time scan
data may be obtained from either a left side of the vehicle or a
right side of the vehicle.
[0015] The localisation reference data may comprise a reference
scan of the lateral environment on a left side of the navigable
element and a right side of the navigable element, and the
localisation reference data for each side of the navigable element
may be stored in a combined data set. Thus, the data from multiple
parts of the navigable network may be stored together in an
efficient data format. The data stored in the combined data set may
be compressed, allowing data for more parts of the navigable
network to be stored within the same storage capacity. Data
compression will also allow a reduced network bandwidth to be used
should the reference scan data be transmitted to the vehicle over a
wireless network connection.
[0016] The comparison of the real time scan data from the left side
of the vehicle with the localisation reference data from the left
side of the navigable element and the comparison of the real time
scan data from the right side of the vehicle with the localisation
reference data from the right side of the navigable element may be
a single comparison. Thus, when the scan data comprises data from
the left side of the navigable element and data from the right side
of the navigable element, the scan data may be compared as a single
data set, significantly reducing the processing requirements
compared to where the comparison for the left side of the navigable
element and the comparison for the right side of the navigable
element are performed separately.
[0017] The longitudinal position of the vehicle in relation to the
digital map may always be known to sub-metre accuracy. Thus, in
some embodiments, the present invention is particularly suitable to
applications requiring high accuracy position estimates, such as
highly automated driving.
[0018] Comparing the real time scan data to the localisation
reference data may comprise calculating a cross-correlation,
preferably a normalised cross-correlation, between the real time
scan data and the localisation reference data.
[0019] The comparison of the real time scan data to the
localisation reference data may be performed over a window of
longitudinal data. Thus, windowing the data allows the comparison
to take account of a subset of the available data. The comparison
may be performed periodically for overlapping windows. At least
some overlap in the windows of data used for the comparison ensures
the differences between neighbouring calculated longitudinal offset
values are smoothed over the data. The window may have a length
sufficient for the accuracy of the offset calculation to be
invariant to transient features, preferably the length being at
least 100 m. Such transient features may be, for example, parked
vehicles, overtaking vehicles or vehicles travelling the same route
in the opposite direction. In some embodiments, the length is at
least 50 m. In some embodiments, the length is 200 m. In this way,
the sensed environment data is determined for a longitudinal
stretch of road, the `window`, e.g. 200 m, and the resultant data
is then compared to the localisation reference data for the stretch
of road. By performing the comparison over a stretch of road of
this size, i.e. one that is substantially larger than the length of
the vehicle, non-stationary or temporary objects, such as other
vehicles on the road, vehicles stopped on the side of the road,
etc, will typically not impact the result of the comparison.
[0020] The real time scan data may be obtained using at least one
range-finder sensor. The range-finder sensor may be configured to
operate along a single axis. The range-finder sensor may be
arranged to perform a scan in a vertical axis. When the scan is
performed in the vertical axis, distance information for planes at
multiple heights is collected, and thus the resultant scan is
significantly more detailed. Alternatively, or in addition, the
range-finder sensor may be arranged to perform a scan in a
horizontal axis.
[0021] The range-finder sensor may be arranged to point in an
outwards direction at substantially 90 degrees to the direction of
travel of the vehicle. Thus, where multiple range-finder sensors
are used, the comparison with the reference scan data may be
carried out in a single comparison for all real time scan data
acquired at the same time.
[0022] The range-finder sensor is configured to obtain data within
an acquisition angle of between 50.degree. and 90.degree.. As used
herein, the term acquisition angle means the total angular field of
view of the range-finder sensor representing the maximum angular
separation possible for two objects which are observable to the
range-finder sensor. In some embodiments, the acquisition angle is
substantially 70 degrees.
[0023] The range-finder sensor may be a laser scanner. The laser
scanner may comprise a laser beam scanned across the lateral
environment using at least one mirror. Thus, the laser scanner may
be positioned away from the surface of the vehicle to protect the
delicate component. In some embodiments, the mirror is actuated to
scan the laser across the lateral environment. Thus, only a
lightweight mirror need by physically rotated, and not the heavier
laser scanner assembly.
[0024] At least a portion of the localisation reference data may be
stored remotely. Preferably, at least a portion of the localisation
reference data is stored locally on the vehicle. Thus, even though
the localisation reference data is available throughout the route,
it need not be continually transferred onto the vehicle and the
comparison may be performed on the vehicle.
[0025] The localisation reference data may be stored in a
compressed format. The localisation reference data may have a size
that corresponds to 30 KB/km or less.
[0026] The localisation reference data may be stored for at least
some, and preferably all, of the navigable elements of the
navigable network represented in the digital map. Thus, the
position of the vehicle can be continually determined anywhere
along the route.
[0027] The reference scan may have been obtained from at least one
device located on a mobile mapping vehicle which has previously
travelled along the navigable element. Thus, the reference scan may
have been acquired using a different vehicle than the current
vehicle for which a position is being continually determined. In
some embodiments, the mobile mapping vehicle is of a similar design
to the vehicle for which the position is being continually
determined.
[0028] In accordance with a second aspect of the present invention,
there is provided a method of generating a reference scan
associated with a digital map; the digital map comprising data
representative of navigable elements (e.g. roads) of a navigable
network (e.g. road network). The method comprises obtaining a
reference scan of the lateral environment along at least one
navigable element represented in the digital map; and determining
actual positions of the reference scan throughout the reference
scan.
[0029] Thus, in accordance with the second aspect of the present
invention for at least one route in the digital map, a reference
scan is obtained all along the route. This obtained reference data
is suitable for using in any of the embodiments of the first aspect
of the present invention.
[0030] The reference scan may be obtained on a left side of the
navigable element and a right side of the navigable element. This
helps to reduce the effect of transient features on a position
estimation which may be performed using the generated reference
scan. Such transient features may be, for example, parked vehicles,
overtaking vehicles or vehicles travelling the same route in the
opposite direction. Obviously, in this case, the transient features
were present when the reference scan data was being acquired. Thus,
reference scan can record features present on both sides of the
route.
[0031] The reference scan data may be obtained using at least one
range-finder sensor. The range-finder sensor may be configured to
operate along a single axis. The range-finder sensor may be
arranged to perform a scan in a vertical axis. When the scan is
performed in the vertical axis, distance information for planes at
multiple heights is collected, and thus the resultant scan is
significantly more detailed. Alternatively, or in addition, the
range-finder sensor may be arranged to perform a scan in a
horizontal axis.
[0032] The range-finder sensor may be arranged to point in an
outwards direction at substantially 90.degree. to the direction of
travel of the vehicle. Thus, where multiple range-finder sensors
are used, the comparison with the reference scan data may be
carried out in a single comparison for all real time scan data
acquired at the same time.
[0033] The range-finder sensor may be configured to obtain data
within an acquisition angle of between 50.degree. and 90.degree..
As used herein, the term acquisition angle means the total angular
field of view of the range-finder sensor representing the maximum
angular separation possible for two objects which are observable to
the range-finder sensor. In some embodiments, the acquisition angle
is substantially 70 degrees.
[0034] The range-finder sensor may be a laser scanner. The laser
scanner may comprise a laser beam scanned across the lateral
environment using mirrors. Thus, the laser scanner may be
positioned away from the surface of the vehicle to protect the
delicate component. In some embodiments, the mirror is actuated to
scan the laser across the lateral environment. Thus, only a
lightweight mirror need by physically rotated, and not the heavier
laser scanner assembly. Additionally, or alternatively, the
range-finder sensor may be radar scanner and/or a pair of stereo
cameras.
[0035] The method may further comprise aligning the reference scan
with the digital map based on the determined actual positions; and
storing the reference scan in a database associated with the
digital map. The actual positions may be determined from an
absolute positioning system, such as a satellite navigation device,
such as GPS, GLONASS, the European Galileo positioning system,
COMPASS positioning system or IRNSS (Indian Regional Navigational
Satellite System). It will be appreciated, however, that other
location determining means can be used, such as using the mobile
telecommunications, surface beacons or the like. The method may
further comprise transmitting the reference scan and the determined
actual positions to a server for subsequent alignment of the
reference scan with the digital map based on the determined actual
positions and storage in a database associated with the digital
map.
[0036] In accordance with a third aspect of the present invention,
there is provided a method of storing reference scan data
associated with a digital map; the digital map comprising data
representative of navigable elements (e.g. roads) of a navigable
network (e.g. road network). The method comprises receiving
localisation reference scan data obtained by scanning a lateral
environment on both sides of a navigable element; and storing
localisation reference data from each side of the navigable element
in a single combined data set.
[0037] Thus, in accordance with the third aspect of the present
invention, data from multiple parts of the navigable element may be
stored together in an efficient data format. The data stored in the
combined data set may be compressed, allowing data for more parts
of the route to be stored within the same storage capacity. Data
compression will also allow a reduced network bandwidth to be used
should the reference scan data be transmitted to the vehicle over a
wireless network connection.
[0038] The method may further comprise transmitting the single
combined data set to a device for determining a longitudinal
position of a vehicle.
[0039] In accordance with a fourth aspect of the present invention,
there is provided a method of determining a longitudinal position
of a vehicle relative to a digital map, the digital map comprising
data representative of navigable elements of a navigable network
along which the vehicle is travelling, the method comprising:
[0040] determining real time scan data by scanning a lateral
environment around the vehicle using at least one range-finder
sensor, said real time scan data comprising one or more depth maps,
each depth map representing the measured lateral distance to
surfaces in the lateral environment for a plurality of longitudinal
positions and elevations;
[0041] retrieving localisation reference data associated with the
digital map for a deemed current longitudinal position of the
vehicle in relation to the digital map, wherein the localisation
reference data comprises one or more depth maps, each depth map
representing the lateral distance to surfaces in the lateral
environment for a plurality of longitudinal positions and
elevations;
[0042] comparing the real time scan data to the localisation
reference data by calculating a cross-correlation to determine a
longitudinal offset between the real time scan data and the
localisation reference data; and
[0043] adjusting the deemed current longitudinal position based on
said longitudinal offset to determine the longitudinal position of
the vehicle relative to the digital map.
[0044] The invention extends to a device, e.g. navigation device,
vehicle, etc, having means, such as one or more processors
arranged, e.g. programmed, to perform any of the methods described
herein. The invention further extends to a non-transitory physical
storage medium containing computer readable instructions executable
to perform or cause a device to perform any of the methods
described herein.
[0045] As will be appreciated by those skilled in the art, the
aspects and embodiments of the present invention can, and
preferably do, include any one or more or all of the preferred and
optional features of the invention described herein in respect of
any of the other aspects of the invention, as appropriate.
BRIEF DESCRIPTION OF THE DRAWINGS
[0046] Embodiments of the invention will now be described, by way
of example only, with reference to the accompanying drawings, in
which:
[0047] FIG. 1 is a representation of a portion of a planning
map;
[0048] FIG. 2 shows a portion of a planning map overlaid on an
image of a road network;
[0049] FIGS. 3 and 4 show an exemplary mobile mapping system that
can be used to collect data for building maps;
[0050] FIG. 5 shows a 3D view of data obtained from a laser
scanner, while FIG. 6 shows a side view projection of the data
obtained from the laser scanner;
[0051] FIG. 7 shows a vehicle, in accordance with an embodiment,
travelling along a road while sensing its surroundings;
[0052] FIG. 8 shows a comparison of localisation reference data
compared to sensed environment data, e.g. as collected by the
vehicle of FIG. 7;
[0053] FIG. 9 shows an exemplary format of how localisation
reference data can be stored;
[0054] FIGS. 10A shows an example point cloud acquired by a
range-finding sensor mounted to a vehicle travelling along a road,
while FIG. 10B shows this point cloud data having been converted
into two depth maps;
[0055] FIG. 11 shows the offset determined following a normalised
cross-correlation calculation in an embodiment;
[0056] FIG. 12 shows another example of a correlation performed
between a "reference" data set and a "local measurement" data set;
and
[0057] FIG. 13 shows an system positioned within a vehicle
according to an embodiment.
DETAILED DESCRIPTION OF THE FIGURES
[0058] It has been recognised that an improved method for
determining the position of a device, such as a vehicle, relative
to a digital map (representative of a navigable network, e.g. road
network) is required. In particular, it is required that the
longitudinal position of the device relative to the digital map can
be accurately determined, e.g. to sub-metre accuracy. The term
"longitudinal" in this application refers to the direction along
the portion of a navigable network on which the device, e.g.
vehicle, is moving; in other words along the length of the road on
which the vehicle is travelling. The term "lateral" in this
application takes its normal meaning as being perpendicular to the
longitudinal direction, and thus refers to the direction along the
width of the road.
[0059] As will be appreciated, when the digital map comprises a
planning map as described above, e.g. a three dimensional vector
model with each lane of a road being representative separately (in
contrast to relative to a centre line for the road as in standard
maps), the lateral position of the device, e.g. vehicle, simply
involves determining the lane in which the device is currently
travelling. Various techniques are known for performing such a
determination. For example, the determination can be made only
using information obtained from the global navigation satellite
system (GNSS) receiver. Additionally or alternatively, information
from a camera, laser or other imaging sensor associated with the
device can be used; for example substantial research has been
carried out in recent years, in which image data from one or more
video cameras mounted within a vehicle is analysed, e.g. using
various image processing techniques, to detect and track the lane
in which the vehicle is travelling. One exemplary technique is set
out in the paper "Multi-lane detection in urban driving
environments using conditional random fields" authored by Junhwa
Hur, Seung-Nam Kang, and Seung-Woo Seo. published in the
proceedings of the Intelligent Vehicles Symposium, page 1297-1302.
IEEE, (2013). Here, the device may be provided with a data feed
from a video camera, radar and/or lidar sensor and an appropriate
algorithm is used to process the received data in real-time to
determine a current lane of the device or the vehicle in which the
device is travelling. Alternatively, another device or apparatus,
such as a Mobileye system available from Mobileye N.V. may provide
the determination of the current lane of the vehicle on the basis
of these data feeds and then feed the determination of the current
lane to the device, for example by a wired connection or a
Bluetooth connection.
[0060] In embodiments, the longitudinal position of the vehicle can
be determined by comparing a real-time scan of the environment
around the vehicle, and preferably on one or both sides of the
vehicle, to a reference scan of the environment that is associated
with the digital map. From this comparison, a longitudinal offset,
if any, can be determined, and the position of the vehicle matched
to the digital map using the determined offset. The position of the
vehicle relative to the digital map can therefore always be known
to a high degree of accuracy.
[0061] The real-time scan of the environment around the vehicle can
be obtained using at least one range-finder sensor that are
positioned on the vehicle. The at least one range-finder sensor can
take any suitable form, but in preferred embodiments comprises a
laser scanner, i.e. a LiDAR device. The laser scanner can be
configured to scan a laser beam across the environment and to
create a point cloud representation of the environment; each point
indicating the position of a surface of an object from which the
laser is reflected. As will be appreciated, the laser scanner is
configured to record the time it takes for the laser beam to return
to the scanner after being reflected from the surface of an object,
and the recorded time can then be used to determine the distance to
each point. In preferred embodiments, the range-finder sensor is
configured to operate along a single axis so as to obtain data
within a certain acquisition angle, e.g. between 50-90.degree.,
such as 70.degree.; for example when the sensor comprises a laser
scanner the laser beam is scanned using mirrors within the
device.
[0062] An embodiment is shown in FIG. 7 in which a vehicle 100 is
travelling along a road. The vehicle is equipped with a
range-finder sensor 101, 102 positioned on each side of the
vehicle. While a sensor is shown on each side of the vehicle, in
other embodiments only a single sensor can be used on one side of
the vehicle. Preferably, the sensors are suitably aligned such that
the data from each sensor can be combined, as is discussed in more
detail below.
[0063] As discussed above, the range-finder sensor(s) can be
arranged to operate along a single axis. In one embodiment, the
sensor can be arranged to perform a scan in a horizontal direction,
i.e. in a plane parallel to the surface of the road. This is shown,
for example, in FIG. 7. By continually scanning the environment as
the vehicle travels along the road, sensed environment data as
shown in FIG. 8 can be collected. The data 200 is the data
collected from the left sensor 102, and shows the object 104. The
data 202 is the data collected from the right sensor 101, and shows
the objects 106 and 108. In other embodiments, the sensor can be
arranged to perform a scan in a vertical direction, i.e. in a plane
perpendicular to the surface of the road. By continually scanning
the environment as the vehicle travels along the road, it is
possible to collect environment data in the manner of FIG. 6. As
will be appreciated, by performing a scan in the vertical
direction, distance information for planes at multiple heights is
collected, and thus the resultant scan is significantly more
detailed. It will of course be appreciated that the scan could be
performed along any axis as desired.
[0064] The reference scan of the environment is obtained from one
or more vehicles that have previously travelled along the road, and
which is then appropriately aligned and associated with the digital
map. The reference scans are stored in a database, which is
associated with the digital map, and are referred to herein as
localisation reference data. The combination of the localisation
reference data when matched to a digital map can be referred to as
a localisation map. As will be appreciated, the localisation map
will be created remotely from the vehicles; typically by a digital
map making company such as TomTom International B.V. or HERE, a
Nokia company.
[0065] The reference scans can be obtained from specialist
vehicles, such as mobile mapping vehicles, e.g. as shown in FIG. 3.
In preferred embodiments, however, the reference scans can be
determined from the sensed environment data that is collected by
vehicles as they travel along the navigable network. This sensed
environment data can be stored and periodically sent to the digital
mapping company to create, maintain and update the localisation
map.
[0066] The localisation reference data is preferably stored locally
at the vehicle, although it will be appreciated that the data could
be stored remotely. In embodiments, and particularly when the
localisation reference data is stored locally, the data is stored
in a compressed format.
[0067] In embodiments, localisation reference data is collected for
each side of a road in the road network. In such embodiments, the
reference data for each side of the road can be stored separately,
or alternatively they can be stored together in a combined data
set.
[0068] In embodiments, the localisation reference data can be
stored as image data. The image data can be colour, e.g. RGB,
images, or greyscale images.
[0069] FIG. 9 shows an exemplary format of how the localisation
reference data can be stored. In this embodiment, the reference
data for the left side of the road is provided on the left side of
the image, and the reference data for the right side of the road is
provided on the right side of the image; the data sets being
aligned such that the left-side reference data set for a particular
longitudinal position is shown opposite the right-side reference
data set for the same longitudinal position.
[0070] In the image of FIG. 9, and for illustrative purposes only,
the longitudinal pixel size is 0.5 m, there are 40 pixels on each
side of the centreline. It has also been determined that the image
can be stored as grayscale images, rather than the colour (RGB)
images. By storing images in this format, the localisation
reference data has a size that corresponds to 30 KB/km.
[0071] A further example can be seen in FIGS. 10A and 10B. FIG. 10A
shows an example point cloud acquired by a range-finding sensor
mounted to a vehicle travelling along a road. In FIG. 10B, this
point cloud data has been converted into two depth maps; one for
the left side of the vehicle and the other for the right side of
the vehicle, which have been placed next to each to form a
composite image.
[0072] As discussed above, the sensed environment data determined
by a vehicle is compared to the localisation reference data to
determine if there is an offset. Any determined offset can then be
used to adjust the position of the vehicle such that it accurately
matched to the correct position on the digital map. This determined
offset is referred to herein as a correlation index.
[0073] In embodiments, the sensed environment data is determined
for a longitudinal stretch of road, e.g. 200 m, and the resultant
data, e.g. image data, then compared to the localisation reference
data for the stretch of road. By performing the comparison over a
stretch of road of this size, i.e. one that is substantially larger
than the length of the vehicle, non-stationary or temporary
objects, such as other vehicles on the road, vehicles stopped on
the side of the road, etc, will typically not impact the result of
the comparison.
[0074] The comparison is preferably performed by calculating a
cross-correlation between the sensed environment data and the
localisation reference data, so as to determine the longitudinal
positions at which the data sets are most aligned. The difference
between the longitudinal positions of both data sets at maximum
alignment allows the longitudinal offset to be determined. This can
be seen, for example, by the offset indicated between the sensed
environment data and localisation reference data of FIG. 8.
[0075] In embodiments, when the data sets are provided as images,
the cross-correlation comprises a normalised cross-correlation
operation, such that differences in brightness, lighting
conditions, etc between the localisation reference data and the
sensed environment data can be mitigated. Preferably, the
comparison is performed periodically for overlapping windows, e.g.
of 200 m lengths, so that any offset is continually determined as
the vehicle travels along the road. FIG. 11 shows the offset
determined, in an exemplary embodiment, following a normalised
cross-correlation calculation between the depicted localisation
reference data and the depicted sensed environment data.
[0076] FIG. 12 illustrates a further example of a correlation
performed between a "reference" data set and a "local measurement"
data set (that is acquired by a vehicle as it travels along a
road). The result of the correlation between the two images can be
seen in the graph of "shift" against "longitudinal correlation
index", wherein the position of the largest peak is used to
determine the illustrated best-fit shift, which can then be used to
adjust the longitudinal position of the vehicle relative to the
digital map.
[0077] As can be seen from FIGS. 9, 10B, 11 and 12, the
localisation reference data and the sensed environment data
preferably are in the form of depth maps, wherein each element
(e.g. pixel when the depth map is stored as an image) comprises: a
first value indicative of a longitudinal position (along a road); a
second value indicative of an elevation (i.e. a height above
ground); and a third value indicative of a lateral position (across
a road). Each element, e.g. pixel, of the depth map therefore
effectively corresponds to a portion of a surface of the
environment around the vehicle. As will be appreciated, the such
that an element, e.g. pixel, will represent a larger surface area
with a higher level of compression of the depth map (or image).
[0078] In embodiments, wherein the localisation reference data is
stored in a data storage means, e.g. memory, of the device, the
comparison step can be performed on one or more processors within
the vehicle. In other embodiments, wherein the localisation
reference data is stored remotely from the vehicle, the sensed
environment data can be sent to a server over a wireless
connection, e.g. via the mobile telecommunications network. The
server, which has access to the localisation reference data, would
then return any determined offset back to the vehicle, e.g. again
using the mobile telecommunications network.
[0079] An exemplary system, according to an embodiment of the
invention, that is positioned within a vehicle is depicted in FIG.
13. In this system, a processing device referred to as a
correlation index provider unit receives a data feed from a
range-finding sensor positioned to detect the environment on the
left side of the vehicle and a range-finding sensor positioned to
detect the environment on the right side of the vehicle. The
processing device also has access to a digital map (that is
preferably in the form of a planning map) and a database of
location reference data, which is suitably matched to the digital
map. The processing device is arranged to perform the method
described above, and thus to compare the data feed from the
range-finding sensors, optionally after converting the data feed
into a suitable form, e.g. an image data combining the data from
both sensors, to localisation reference data to determine a
longitudinal offset and thus accurate position the vehicle relative
to the digital map. The system also comprises a horizon provider
unit, and which uses the determined position of the vehicle and
data within the digital map to provide information (referred to as
a "horizon data") concerning the upcoming portion of the navigable
network that the vehicle is about to traverse. This horizon data
can then be used to control one or more systems within the vehicle
to perform various assisted or automated driving operations, e.g.
adaptive cruise control, automatic lane changing, emergency brake
assistance, etc.
[0080] In summary, the invention relates, at least in preferred
embodiments, to a positioning method based on longitudinal
correlation. The 3D space around a vehicle is represented in the
form of two depth maps, covering both the left and right sides of
the road, and which may be combined into a single image. Reference
images stored in a digital map are cross-correlated with the depth
maps derived from lasers or other range-finding sensors of the
vehicle to position the vehicle precisely along (i.e.
longitudinally) the representation of the road in the digital map.
The depth information can then be used, in embodiments, to position
the car across (i.e. laterally) the road.
[0081] In a preferred implementation, the 3D space around a vehicle
is projected to two grids parallel to road trajectory and the
values of projections are averaged within each cell of the grid. A
pixel of the longitudinal correlator depth map has dimensions of
about 50 cm along the driving direction and about 20 cm height. The
depth, coded by pixel value, is quantized with about 10 cm.
Although the depth map image resolution along the driving direction
is 50 cm, the resolution of positioning is much higher. The
cross-correlated images represent a grid in which the laser points
are distributed and averaged. Proper up-sampling enables finding
shift vectors of sub-pixel coefficients. Similarly, the depth
quantization of about 10 cm does not imply 10 cm precision of
positioning across the road as the quantization error is mostly by
laser precision and calibration, with only very little contribution
from quantization error of longitudinal correlator index.
[0082] Accordingly, it will be appreciated, that the positioning
information, e.g. the depth maps (or images), is always available
(even if no sharp objects are available in the surroundings),
compact (storing whole world's road network is possible), and
enables precision comparable or even better than other approaches
(due to its availability at any place and therefore high error
averaging potential).
[0083] Any of the methods in accordance with the present invention
may be implemented at least partially using software e.g. computer
programs. The present invention thus also extends to a computer
program comprising computer readable instructions executable to
perform, or to cause a navigation device to perform, a method
according to any of the aspects or embodiments of the invention.
Thus, the invention encompasses a computer program product that,
when executed by one or more processors, cause the one or more
processors to generate suitable images (or other graphical
information) for display on a display screen. The invention
correspondingly extends to a computer software carrier comprising
such software which, when used to operate a system or apparatus
comprising data processing means causes, in conjunction with said
data processing means, said apparatus or system to carry out the
steps of the methods of the present invention. Such a computer
software carrier could be a non-transitory physical storage medium
such as a ROM chip, CD ROM or disk, or could be a signal such as an
electronic signal over wires, an optical signal or a radio signal
such as to a satellite or the like. The present invention provides
a machine readable medium containing instructions which when read
by a machine cause the machine to operate according to the method
of any of the aspects or embodiments of the invention.
[0084] Where not explicitly stated, it will be appreciated that the
invention in any of its aspects may include any or all of the
features described in respect of other aspects or embodiments of
the invention to the extent they are not mutually exclusive. In
particular, while various embodiments of operations have been
described which may be performed in the method and by the
apparatus, it will be appreciated that any one or more or all of
these operations may be performed in the method and by the
apparatus, in any combination, as desired, and as appropriate.
* * * * *